应用径向基函数神经网络预测单桩极限承载力  

Application of RBF Neural Network in Estimating Vertical Ultimate Bearing Capacity of Single Piles

作　　者：郑永保[1] 陈建功[1] 董占文 

机构地区：[1]重庆大学土木工程学院,重庆400045 [2]山西吕梁汾柳高速公路建设有限公司,山西省离石市033000

出　　处：《地下空间》2004年第4期519-521,共3页Underground Space

摘　　要：在径向基函数神经网络的基本原理基础上,通过对影响单桩极限承载力因素的分析,建立了单桩极限承载力设计的GRNN模型,并进行了实例分析。计算结果具有较高的精度和收敛速度快等特点,是一种行之有效的预测单桩极限承载力方法。In this paper, based on the main principle of RBF Neural Network, the affecting elements of the bearing capacity of single piles are analyzed. The GRNN model of ultimate bearing capacity of single piles is founded. By means of neural network toolbox of MATLAB, an example is calculated. The results are more accurate with higher convergent rate. This method is practical and effective in estimating vertical ultimate bearing capacity of single piles

关 键 词：单桩极限承载力 基础 计算结果 实例分析 预测 因素 影响 模型 

分 类 号：TU473[建筑科学—结构工程]